Buscar
Mostrando ítems 1-10 de 77
Support vector machine ensembles for discriminant analysis for ranking principal components
(2020-07-05)
The problem of ranking linear subspaces in principal component analysis (PCA), for multiclass classification tasks, has been addressed by building support vector machine (SVM) ensembles and AdaBoost.M2 technique. This ...
An improved ECG-derived respiration method using kernel principal component analysis
Recent studies show that principal component analysis (PCA) of heart beats generates well-performing ECG-derived respiratory signals (EDR). This study aims at improving the performance of EDR signals using kernel PCA (kPCA). ...
Nested AdaBoost procedure for classification and multi-class nonlinear discriminant analysis
(2020-07-12)
AdaBoost methods find an accurate classifier by combining moderate learners that can be computed using traditional techniques based, for instance, on separating hyperplanes. Recently, we proposed a strategy to compute each ...
Nested AdaBoost procedure for classification and multi-class nonlinear discriminant analysis
(2020-07-12)
AdaBoost methods find an accurate classifier by combining moderate learners that can be computed using traditional techniques based, for instance, on separating hyperplanes. Recently, we proposed a strategy to compute each ...
Multivariate analyses of weight traits fitting reduced rank and factor analytic models in Nellore cattle
(Elsevier B.V., 2010-01-01)
Multivariate analyses of weight traits fitting reduced rank and factor analytic models in Nellore cattle
(Elsevier B.V., 2010-01-01)
A new ranking method for principal components analysis and its application to face image analysis
(2010)
In this work, we investigate a new ranking method for principal component analysis (PCA). Instead of sorting the principal components in decreasing order of the corresponding eigenvalues, we propose the idea of using the ...
A new ranking method for principal components analysis and its application to face image analysis
(2010)
In this work, we investigate a new ranking method for principal component analysis (PCA). Instead of sorting the principal components in decreasing order of the corresponding eigenvalues, we propose the idea of using the ...